The prevalence of suicidal behavior and ideation during pregnancy and postpartum period, its variation in the COVID-19 pandemic, and the related factors: A systematic review and meta-analysis of observational studies

Bacground Pregnancy and postpartum are sensitive periods for mental health problems due to increased stressors and demands, and the prevalence of intentional self-harming behaviors such as suicidal behavior and ideation may increase. Changes in the provision of prenatal care services and utilization of health services and adverse living conditions during the COVID-19 epidemic may also trigger or exacerbate mental illnesses. Aims To investigate the prevalence of suicidal behavior and ideation encountered during pregnancy and postpartum period, its change in the COVID-19 pandemic, and the related factors. Methods A systematic review and meta-analysis of observational studies was conducted. A search was conducted in April 2021 and updated in April 2023 on Web of Science, PubMed, PsycINFO, EBSCO, Turk Medline, Turkish Clinics, and ULAKBIM databases. Two authors independently conducted the search, selection of articles, data extraction, and quality assessment procedures, and an experienced researcher controlled all these steps. Joanna Briggs Institute's Critical Appraisal Checklists were used to assess the quality of the studies. Results The meta-analysis included 38 studies and the total sample size of the studies was 9 044 991. In this meta-analysis, the prevalence of suicidal behavior in women during pregnancy and postpartum periods was 5.1 % (95 % CI, 0.01–1.53), suicidal ideation 7.2 % (95 % CI, 0.03–0.18), suicide attampt 1 % (95 % CI, 0.00–0.07) and suicidal plan 7.8 % (95 % CI, 0.06–0.11). Rate of suicidal behavior, ideation/thought increased and attempts in the pandemic process (2.5% vs 19.7 %; 6.3% vs 11.3 %; 3.6% vs 1.4 %, respectively). Prevalences of suicidal behavior, ideation, attempts, and plan in the postpartum period was higher than during pregnancy (1.1% vs 23.4 %; 6.1% vs 9.2 %; 0.5% vs 0.7 %; 7.5% vs 8.8 %, respectively). ... (AU)

Brazilian Dialysis Survey 2022 / Censo Brasileiro de Diálise 2022

Abstract Introduction: The Brazilian Dialysis Survey (BDS) is an annual national survey about patients on chronic dialysis that contributes to health policies. Objective: To report the 2022 epidemiological data from the BDS of the Brazilian Society of Nephrology (BSN). Methods: A survey was carried out in Brazilian chronic dialysis centers using an online questionnaire that included clinical and epidemiological aspects of patients on chronic dialysis, dialysis therapy data, and dialysis center characteristics. Results: Overall, 28% (n = 243) of the centers answered the questionnaire. In July 2022, the estimated total number of patients on dialysis was 153,831. The estimated prevalence and incidence rates of patients per million population (pmp) were 758 and 214, respectively. Of the prevalent patients, 95.3% were on hemodialysis (HD, 4.6% of these on hemodiafiltration) and 4.7% on peritoneal dialysis (PD). Only 1.3% of patients were not vaccinated against COVID-19. The prevalence of anemia (Hb < 10g/dL) was 27% and hyperphosphatemia (P > 5.5mg/dL) reached 30%. The estimated overall crude annual mortality rate was 17.1%. Conclusions: The absolute number and prevalence rate of patients on chronic dialysis continue to increase. A growing number of patients were receiving hemodiafiltration. The mortality rate decreased, probably due to the end of COVID-19 pandemic. The conclusions were drawn in the context of relatively low voluntary participation, which imposed methodological limitations on our estimates.

Resumo Introdução: O Censo Brasileiro de Diálise (CBD) é uma pesquisa nacional anual sobre pacientes em diálise crônica que contribui para as políticas de saúde. Objetivo: Informar os dados epidemiológicos de 2022 do CBD da Sociedade Brasileira de Nefrologia (SBN). Métodos: Foi realizada uma pesquisa em centros brasileiros de diálise por meio de um questionário online que incluiu aspectos clínicos e epidemiológicos de pacientes em diálise crônica, dados da terapia dialítica e características do centro de diálise. Resultados: No total, 28% (n = 243) dos centros de diálise ativos cadastrados na SBN responderam ao questionário. Em julho de 2022, o número total estimado de pacientes em diálise era de 153.831. As taxas estimadas de prevalência e incidência de pacientes por milhão (ppm) de habitantes foram 758 e 214, respectivamente. Dos pacientes prevalentes, 95,3% estavam em hemodiálise (HD; 4,6% desses em hemodiafiltração) e 4,7% em diálise peritoneal (DP). Apenas 1,3% dos pacientes não foram vacinados contra a COVID-19. A prevalência de anemia (Hb < 10g/dL) foi de 27% e de hiperfosfatemia (P > 5,5mg/dL) alcançou 30%. A taxa bruta total anual de mortalidade estimada foi de 17,1%. Conclusões: O número absoluto e a taxa de prevalência de pacientes em diálise crônica continuam a aumentar. Um número crescente de pacientes estava em hemodiafiltração. A taxa de mortalidade diminuiu, provavelmente devido ao fim da pandemia da COVID-19. As conclusões foram de um contexto de participação voluntária relativamente baixa, o que impõe limitações metodológicas às nossas estimativas.

Prognostic significance of the chemerin level in coronavirus disease 2019 patients.

Increased serum chemerin levels have been reported in several inflammatory diseases. Few studies have investigated the relationship between chemerin and clinical features of COVID-19. Thus, chemerin may modulate the development and progression of COVID-19. We compared the serum chemerin concentration between patients with and without SARS-CoV-2 infection and its association with the severity and prognosis of COVID-19 pneumonia. This is a prospective, single-center, cross-sectional study. We enrolled COVID-19 patients who presented to our tertiary hospital and healthy controls. The COVID-19 patients were conducted and the dates of symptom onset were recorded. After admission to the hospital and stabilization, blood samples were obtained for routine hemogram, biochemistry, and chemerin. The chemerin level was 37.93 ±â€…17.3 ng/mL in patients followed in the ICU, 29.41 ±â€…12.79 ng/mL in inpatients, 30.48 ±â€…10.86 ng/mL in outpatients, and 25.12 ±â€…9.82 ng/mL in healthy controls. The difference between patients treated in the ICU and healthy controls was significant (P < .001). The high-sensitivity C-reactive protein (hs-CRP), ferritin, procalcitonin (PCT), and D-dimer levels were significantly higher in the intensive care unit (ICU) group (P < .001). Moreover, the chemerin level of patients who died was significantly higher than that of those who survived (P < .001). The chemerin level was increased in COVID-19 patients and also increased with increasing disease severity. The chemerin level was higher in the COVID-19 patients than healthy controls and was significantly higher in patients who died compared to those who did not.

Greater within- and between-day instability is associated with worse anxiety and depression symptoms.

BACKGROUND: Depression and anxiety affect hundreds of millions of people worldwide, and their prevalence increased during the COVID-19 pandemic as social schedules were disrupted. This study explores the associations between anxiety and depression and within- and between-day instability of affective, somatic, and cognitive symptoms during the early pandemic stages. METHODS: Participants (n = 153, ages 18-77, 72 % female) reported daily levels of affective (anxiety/sadness), somatic (appetite/sleepiness), and cognitive (concentration/energy) symptoms for 14-44 days at five timepoints: 0, 3, 6, 9, and 12 h after awakening. At the end of the study, participants completed validated scales for anxiety (GAD-7) and depression (PHQ-9). Symptom instability was assessed using the Absolute Real Variability (ARV) index. Regression models examined within-day instability (WD-I) and between-day instability (BD-I) with GAD-7 and PHQ-9 scores as outcomes. RESULTS: Greater instability (both WD-I and BD-I) of affective symptoms correlated with elevated GAD-7 and PHQ-9 scores. For somatic and cognitive symptoms, greater BD-I was associated with higher scores. LIMITATIONS: The study used retrospective daily data, which could benefit from real-time assessments for improved accuracy. CONCLUSIONS: This study provides empirical evidence of a connection between greater anxiety and depression severity and increased instability in daily mood and physiological symptoms. The findings underscore the importance of consistent symptom monitoring to understand overall mental health trajectories. Additionally, it highlights the role of daily routines in stabilizing the circadian system, potentially regulating physiological and psychological processes and reducing symptom instability.

COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study.

BACKGROUND: Air pollution and a number of metabolic disorders have been reported to increase the risk of severe COVID-19 outcomes. This study explored the association between severe COVID-19 outcomes, metabolic disorders and environmental air pollutants, at regional level, across 38 countries. METHODS: We conducted an ecological study using COVID-19 data related to countries of the Organization for Economic Cooperation and Development (OECD), with an estimated population of 1.4 billion. They were divided into 3 regions: 1. Europe & Middle east; 2. Americas (north, central & south America); 3. East-Asia & West Pacific. The outcome variables were: COVID-19 case-fatality rate (CFR) and disability-adjusted life years (DALYs) at regional level. Freely accessible datasets related to regional DALYs, demographics and other environmental pollutants were obtained from OECD, WHO and the World in Data websites. Generalized linear model (GLM) was performed to determine the regional determinants of COVID-19 CFR and DALYs using the aggregate epidemiologic data (Dec. 2019-Dec. 2021). RESULTS: Overall cumulative deaths were 65,000 per million, for mean CFR and DALYs of 1.31 (1.2)% and 17.35 (2.3) years, respectively. Globally, GLM analysis with adjustment for elderly population rate, showed that COVID-19 CFR was positively associated with atmospheric PM2.5 level (beta = 0.64(0.0), 95%CI: 0.06-1.35; p < 0.05), diabetes prevalence (beta = 0.26(0.1), 95%CI: 0.12-0.41; p < 0.001). For COVID-19 DALYs, positive associations were observed with atmospheric NOx level (beta = 0.06(0.0), 95%CI: 0.02-0.82; p < 0.05) and diabetes prevalence (beta = 0.32(0.2), 95%CI: 0.04-0.69; p < 0.05). At regional level, adjusted GLM analysis showed that COVID-19 CFR was associated with atmospheric PM2.5 level in the Americas and East-Asia & Western Pacific region; it was associated with diabetes prevalence for countries of Europe & Middle east and East-Asia & Western Pacific region. Furthermore, COVID-19 DALYs were positively associated with atmospheric PM2.5 and diabetes prevalence for countries of the Americas only. CONCLUSION: These findings confirm that diabetes and air pollution increase the risk of disability and fatality due to COVID-19, with disparities in terms of their impact. They suggest that efficient preventive and management programs for diabetes and air pollution countermeasures would have curtailed severe COVID-19 outcome rates.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Methods: Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Results: Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Conclusion: Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

Interleukin inhibitors and the associated risk of candidiasis.

Interleukins (ILs) are vital in regulating the immune system, enabling to combat fungal diseases like candidiasis effectively. Their inhibition may cause enhanced susceptibility to infection. IL inhibitors have been employed to control autoimmune diseases and inhibitors of IL-17 and IL-23, for example, have been associated with an elevated risk of Candida infection. Thus, applying IL inhibitors might impact an individual's susceptibility to Candida infections. Variations in the severity of Candida infections have been observed between individuals with different IL inhibitors, necessitating careful consideration of their specific risk profiles. IL-1 inhibitors (anakinra, canakinumab, and rilonacept), IL-2 inhibitors (daclizumab, and basiliximab), and IL-4 inhibitors (dupilumab) have rarely been associated with Candida infection. In contrast, tocilizumab, an inhibitor of IL-6, has demonstrated an elevated risk in the context of coronavirus disease 2019 (COVID-19) treatment, as evidenced by a 6.9% prevalence of candidemia among patients using the drug. Furthermore, the incidence of Candida infections appeared to be higher in patients exposed to IL-17 inhibitors than in those exposed to IL-23 inhibitors. Therefore, healthcare practitioners must maintain awareness of the risk of candidiasis associated with using of IL inhibitors before prescribing them. Future prospective studies need to exhaustively investigate candidiasis and its associated risk factors in patients receiving IL inhibitors. Implementing enduring surveillance methods is crucial to ensure IL inhibitors safe and efficient utilization of in clinical settings.

Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality.

There is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1-5.3), mainly driven by an increase among girls aged 8-17 (trend variation 1.8, 95%CI 1.2-2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16-1.48; 1.3, 95%CI 1.10-1.64 and 1.7, 95%CI 1.48-1.98), fewer patients died (p = 0.007) and stays were shorter (p < 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic.

The impact of the COVID-19 pandemic on renal cancer care.

PURPOSE: To evaluate the impact of the COVID-19 pandemic on renal cell carcinoma (RCC) care in the Netherlands. METHODS: Newly diagnosed RCCs between 2018 and 2021 were selected from the Netherlands Cancer Registry; 2020-2021 was defined as COVID period and 2018-2019 as reference period. Numbers of RCCs were evaluated using 3-week-moving averages, overall and by disease stage and age. Changes in treatment were evaluated with logistic regression analyses. To evaluate possible delays in care, time to start of treatment was assessed. The cumulative number of metastatic RCC (mRCC) over time was assessed to evaluate stage shift. RESULTS: During the 1st COVID wave (weeks 9-22, 2020), the number of new RCC diagnoses decreased with 15%. Numbers restored partially in 2020, but remained 10% lower compared to 2018/2019. The decline was mostly due to a drop in T1a/T1b RCCs and in age > 70 years. 2021 showed similar numbers of new RCC diagnoses compared to 2018/2019 without an increase due to previously missed RCCs. Treatment-related changes during the 1st COVID wave were limited and temporarily; less surgery in T1a RCCs in favor of more active surveillance, and in mRCC targeted therapy was preferred over immunotherapy. Time to start of firstline treatment was not prolonged during the 1st COVID wave. No increase in mRCC was found until the end of 2021. CONCLUSIONS: The COVID-19 pandemic resulted in fewer RCC diagnoses, especially T1a/T1b tumors. Treatment-related changes appeared to be limited, temporarily and in accordance with the adapted guidelines. The diagnostic delay could lead to more advanced RCCs in later years but there are no indications for this yet.

Community health worker outreach to farmworkers in rural North Carolina: Learning from adaptations to the SARS-CoV-2 pandemic.

BACKGROUND: Community health workers represent a critical part of the health outreach and services for migrant and seasonal farmworkers ('farmworkers') in rural areas of the United States. PURPOSE: We sought to identify adaptations to farmworker patient engagement and health outreach made by community health workers during the first 18 months of the COVID-19 pandemic. METHODS: In this qualitative study, we used semi-structured interviews with community health workers from August 2020 to February 2022 (n = 21). Two coders used thematic analysis to identify three themes related to the experiences of community health workers in conducting health education and outreach to farmworkers prior to and following the onset of the pandemic. FINDINGS: We found themes related to pre-pandemic outreach efforts to provide health education resource sharing with farmworkers and pandemic-related outreach efforts that included adoption of porch drops and distanced delivery of health education, adaptation of modes of health education and communication through technology and the internet, and taking on new roles related to COVID-19. Finally, we identified changes that reverted after the pandemic or will continue as adaptations. CONCLUSIONS: Community health workers created practice-based innovations in outreach in response to the COVID-19 pandemic. These innovations included new COVID-19 related roles and new modes of health education and outreach, including the use of digital resources. The changes developed for emergency use in COVID-19, particularly related to internet and technology, have likely altered how community health workers conduct outreach in North Carolina going forward. Funders, community health worker training programs, and researchers should take note of these innovations. PATIENT OR PUBLIC CONTRIBUTION: Community health workers who typically come from patient populations and provide critical navigation and connection with the health care system advised on the design and creation of this research project, including serving on an advisory board. Two authors have experience working as community health workers.

Harnessing technology to improve sleep in frontline healthcare workers: A pilot study of electronic noise-masking earbuds on subjective and objective sleep measures.

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen's d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen's d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.

Trust-building in temporary public health partnerships: a qualitative study of the partnership formation process of a Covid-19 test, trace and protect service.

BACKGROUND: Public health initiatives require coordinated efforts from healthcare, social services and other service providers. Organisational theory tells us that trust is essential for reaching collaborative effectiveness. This paper explores the drivers for initiating and sustaining trust in a temporary public health partnership, in response to a sudden health threat. METHODS: This qualitative study analysed the formation process of a multisector partnership for a Covid-19 contact tracing service. Data was collected through 12 interviews, two focus groups, one feedback workshop, and an online survey with workforce members from all seven partner organisations. Purposive maximum variation sampling was used to capture the reflections and experiences of workforce members from all seven partner organisations. A deductive code scheme was used to identify drivers for building and sustaining trust in inter-organisational collaboration. RESULTS: Relational mechanisms emanating from the commitment to the common aim, shared norms and values, and partnership structures affected trust-building. Shared values and the commitment to the common aim appeared to channel partners' behaviour when interacting, resulting in being perceived as a fair, reliable and supportive partner. Shared values were congruent with the design of the partnership in terms of governance structure and communication lines reflecting flat hierarchies and shared decision-making power. Tensions between partner organisations arose when shared values were infringed. CONCLUSIONS: When managing trust in a collaboration, partners should consider structural components like governance structure, organisational hierarchy, and communication channels to ensure equal power distribution. Job rotation, recruitment of candidates with the desired personality traits and attitudes, as well as training and development, encourage inter-organisational networking among employees, which is essential for building and strengthening relationships with partner organisations. Partners should also be aware of managing relational dynamics, channelling behaviours through shared values, objectives and priorities and fostering mutual support and equality among partner organisations.

Changes in long-term care insurance revenue among service providers during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has impacted peoples' health-related behaviors, especially those of older adults, who have restricted their activities in order to avoid contact with others. Moreover, the pandemic has caused concerns in long-term care insurance (LTCI) providers regarding management and financial issues. This study aimed to examine the changes in revenues among LTCI service providers in Japan during the pandemic and analyze its impact on different types of services. METHODS: In this study, we used anonymized data from "Kaipoke," a management support platform for older adult care operators provided by SMS Co., Ltd. Kaipoke provides management support services to more than 27,400 care service offices nationwide and has been introduced in many home-care support offices. The data used in this study were extracted from care plans created by care managers on the Kaipoke platform. To examine the impact of the pandemic, an interrupted time-series analysis was conducted in which the date of the beginning of the pandemic was set as the prior independent variable. RESULTS: The participating providers were care management providers (n = 5,767), home-visit care providers (n = 3,506), home-visit nursing providers (n = 971), and adult day care providers (n = 4,650). The results revealed that LTCI revenues decreased significantly for care management providers, home-visit nursing providers, and adult day care providers after the COVID-19 pandemic began. The largest decrease was an average base of USD - 1668.8 in adult day care. CONCLUSION: The decrease in revenue among adult day care providers was particularly concerning in terms of the sustainability of their business. This decrease in revenue may have made it difficult to retain personnel, and staff may have needed to be laid off as a result. Although this study has limitations, it may provide useful suggestions for countermeasures in such scenarios, in addition to support conducted measures.

Burdens and resources of Austrian clinical psychologists: results of a qualitative study two years into the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic increased the mental health burden in the general population, enhancing the demands placed on mental healthcare professionals. METHODS: This study aimed to assess the burdens and resources of clinical psychologists that emerged since the beginning of the pandemic. N = 172 Austrian clinical psychologists participated in a cross-sectional online survey between April and May 2022. The burdens and the sources of support that emerged during the pandemic were analyzed using qualitative content analysis. RESULTS: Mental health-related issues were identified as the greatest burden, followed by work-related themes and restrictions imposed by the government to combat the spreading of the virus. The most important resources mentioned by the clinical psychologists were social contacts and recreational activities. Practising mindfulness and focusing on inner processes and work-related aspects were further important resources mentioned. CONCLUSION: Overall, it seems that clinical psychologists have a high awareness of mental health-related problems related to the pandemic and use adaptive coping strategies to deal with them.

Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques.

The COVID-19 pandemic has profoundly reshaped human life. The development of COVID-19 vaccines has offered a semblance of normalcy. However, obstacles to vaccination have led to substantial loss of life and economic burdens. In this study, we analyze data from a prominent health insurance provider in the United States to uncover the underlying reasons behind the inability, refusal, or hesitancy to receive vaccinations. Our research proposes a methodology for pinpointing affected population groups and suggests strategies to mitigate vaccination barriers and hesitations. Furthermore, we estimate potential cost savings resulting from the implementation of these strategies. To achieve our objectives, we employed Bayesian data mining methods to streamline data dimensions and identify significant variables (features) influencing vaccination decisions. Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, demonstrating superior performance.

Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease.

INTRODUCTION: Reliable assessment of individuals with Parkinson's disease (PD) is essential for providing adequate treatment. Clinical assessment is a complex and time-consuming task, especially for bradykinesia, since its evaluation can be influenced by the degree of experience of the examiner, patient collaboration and individual bias. Improvement of the clinical evaluation can be obtained by considering assessments from several professionals. However, this is only true when inter and intra-rater agreement are high. Recently, the Movement Disorder Society highlighted, during the COVID-19 pandemic, the need to develop and validate technologies for remote assessment of the motor status of people with PD. Thus, this study introduces an objective strategy for the remote evaluation of bradykinesia using multi-specialist analysis. METHODS: Twelve volunteers with PD participated and these were asked to execute finger tapping, hand opening/closing and pronation/supination movements. Each task was recorded and rated by fourteen PD health experts for each patient. The scores were assessed on an individual basis. Intra and inter-rater agreement and correlation were estimated. RESULTS: The results showed that agreements and correlations between experienced examiners were high with low variability. In addition, group analysis was noted as possessing the potential to solve individual inconsistency bias. CONCLUSION: Furthermore, this study demonstrated the need for a group with prior training and experience, along with indicating the importance for the development of a clinical protocol that can use telemedicine for the evaluation of individuals with PD, as well as the inclusion of a specialized mediating group. In Addition, this research helps to the development of a valid remote assessment of bradykinesia.

COVID-19 inequalities in England: a mathematical modelling study of transmission risk and clinical vulnerability by socioeconomic status.

BACKGROUND: The COVID-19 pandemic resulted in major inequalities in infection and disease burden between areas of varying socioeconomic deprivation in many countries, including England. Areas of higher deprivation tend to have a different population structure-generally younger-which can increase viral transmission due to higher contact rates in school-going children and working-age adults. Higher deprivation is also associated with a higher presence of chronic comorbidities, which were convincingly demonstrated to be risk factors for severe COVID-19 disease. These two major factors need to be combined to better understand and quantify their relative importance in the observed COVID-19 inequalities. METHODS: We used UK Census data on health status and demography stratified by decile of the Index of Multiple Deprivation (IMD), which is a measure of socioeconomic deprivation. We calculated epidemiological impact using an age-stratified COVID-19 transmission model, which incorporated different contact patterns and clinical health profiles by decile. To separate the contribution of each factor, we considered a scenario where the clinical health profile of all deciles was at the level of the least deprived. We also considered the effectiveness of school closures and vaccination of over 65-year-olds in each decile. RESULTS: In the modelled epidemics in urban areas, the most deprived decile experienced 9% more infections, 13% more clinical cases, and a 97% larger peak clinical size than the least deprived; we found similar inequalities in rural areas. Twenty-one per cent of clinical cases and 16% of deaths in England observed under the model assumptions would not occur if all deciles experienced the clinical health profile of the least deprived decile. We found that more deaths were prevented in more affluent areas during school closures and vaccination rollouts. CONCLUSIONS: This study demonstrates that both clinical and demographic factors synergise to generate health inequalities in COVID-19, that improving the clinical health profile of populations would increase health equity, and that some interventions can increase health inequalities.

Impact of metabolic dysfunction-associated steatotic liver disease on COVID-19 hospitalizations: A propensity-matched analysis of the United States.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), formally known as nonalcoholic fatty liver disease, is the most common chronic liver disease in the United States. Patients with MASLD have been reported to be at a higher risk of developing severe coronavirus disease 2019 (COVID-19) and death. However, most studies are single-center studies, and nationwide data in the United States is lacking. AIM: To study the influence of MASLD on COVID-19 hospitalizations during the initial phase of the pandemic. METHODS: We retrospectively analyzed the 2020 National Inpatient Sample (NIS) database to identify primary COVID-19 hospitalizations based on an underlying diagnosis of MASLD. A matched comparison cohort of COVID-19 hospitalizations without MASLD was identified from NIS after 1: N propensity score matching based on gender, race, and comorbidities, including hypertension, heart failure, diabetes, and cirrhosis. The primary outcomes included inpatient mortality, length of stay, and hospitalization costs. Secondary outcomes included the prevalence of systemic complications. RESULTS: A total of 2210 hospitalizations with MASLD were matched to 2210 hospitalizations without MASLD, with a good comorbidity balance. Overall, there was a higher prevalence of severe disease with more intensive care unit admissions (9.5% vs 7.2%, P = 0.007), mechanical ventilation (7.2% vs 5.7%, P = 0.03), and septic shock (5.2% vs 2.7%, P <0.001) in the MASLD cohort than in the non-MASLD cohort. However, there was no difference in mortality (8.6% vs 10%, P = 0.49), length of stay (5 d vs 5 d, P = 0.25), and hospitalization costs (42081.5 $ vs 38614$, P = 0.15) between the MASLD and non-MASLD cohorts. CONCLUSION: The presence of MAFLD with or without liver cirrhosis was not associated with increased mortality in COVID-19 hospitalizations; however, there was an increased incidence of severe COVID-19 infection. This data (2020) predates the availability of COVID-19 vaccines, and many MASLD patients have since been vaccinated. It will be interesting to see if these trends are present in the subsequent years of the pandemic.

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis.

BACKGROUND: The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information. AIM: To identify key factors that may explain the variability in case lethality across countries. METHODS: We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship. RESULTS: The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR. CONCLUSION: The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

Respiratory pathogenic microbial infections: a narrative review.

Respiratory infectious diseases have long been recognised as a substantial global healthcare burden and are one of the leading causes of death worldwide, particularly in vulnerable individuals. In the post COVID-19 era, there has been a surge in the prevalence of influenza virus A and other multiple known viruses causing cold compared with during the same period in the previous three years, which coincided with countries easing COVID-19 restrictions worldwide. This article aims to review community-acquired respiratory illnesses covering a broad spectrum of viruses, bacteria, and atypical microorganisms and focuses on the cluster prevalence of multiple known respiratory pathogens in China, thereby providing effective prevention and control measures.